Whole -genome survival analysis of 144 286 people from the UK Biobank identifies novel loci associated with blood pressure

This study utilized UK Biobank data from 144 286 participants and employed whole-genome sequencing (WGS) data and time-to-event data over a 12-year follow-up period to identify susceptibility in genetic variants associated with hypertension. Following genotype quality control, 6 319 822 single nucleotide polymorphisms underwent analysis, revealing 31 significant variant-level associations. Among these, 29 were novel – 15 in Fibrillin-2 (FBN2) and 4 in Junctophilin-2 (JPH2). Mendelian randomization utilizing two identified variants (rs17677724 and rs1014754) suggested that a genetically induced decrease in heart FBN2 expression and an increase in adrenal gland JPH2 expression were causally linked to hypertension. Phenome-wide association (PheWAS) analysis using the FinnGen dataset confirmed positive associations of rs17677724 and rs1014754 with hypertension, assessed across 2727 traits in 377 277 individuals. Lastly, rs1014754 positively associated with kallistatin, whereas rs17677724 negatively associated with renin in the Fenland study, suggesting a counterregulatory response to high blood pressure. This study, employing WGS data, identified novel genetic loci and potential therapeutic targets for hypertension.

time of conducting this analysis, whole-genome sequencing data was available for 200 005 participants, constituting the dataset utilized for this study.Stringent quality control measures were implemented to assess sample integrity, including evaluations of missingness, heterozygosity, genotype call rate outliers, sex-discordance, non-European ancestry through principal component analysis, pregnancy status, retracted consent, sex chromosome aneuploidy, and sample relatedness.Additionally, samples with preexisting hypertension before the follow-up period and missing covariate data were excluded from the analysis.Samples passing quality control had their genetic data undergo further stringent genetic quality control measures across 22 autosomal chromosomes.Exclusion criteria included genotype missing rates over 0.1, deviations from Hardy-Weinberg Equilibrium (HWE) at 1 Â 10 À15 significance, variants with minor allele count (MAC) below 100, and minor allele frequency (MAF) under 0.01. Figure 1 provides a summary of this quality control process.
The time-to-event analysis commenced from the enrolment of the final UK Biobank participant on 1 October 2010, extending until the latest diagnosis of 'Essential-hypertension' within the cohort on 1 October 2022, comprising approximately 12 years (4383 days) of follow-up.The SPA-COX package in R-facilitated genome-wide survival analysis, identifying loci associated with hypertension using a saddle point approximation implementation of a Cox proportional hazards regression model [4].The analysis accounted for the following covariates: age, age 2 , BMI, genetic batch, sex, and 10 principal components, with significant loci identified using a stringent P value threshold of less than 5 Â 10 À8 .
To discover novel hypertension-associated SNPs, we compared significant SNPs from our study with those previously identified in BP GWAS using the GWAS catalogue (https://www.ebi.ac.uk/gwas/).We utilized Ensembl (https://www.ensembl.org/index.html) to determine the associated genes and investigated whether these SNPs functioned as expression quantitative trait loci (eQTLs) in-cis, particularly focusing on organs relevant to BP regulation using Genotype-Tissue Expression (GTEx) resource (https://gtexportal.org/home/) [5].Further investigation involved interrogating the tissue-dependent Mendelian randomization atlas (http://mrcieu.mrsoftware.org/Tissue_MR_atlas/), to establish potential causal links between identified SNPs and hypertension, spanning 395 complex traits and diseases.Lastly, we evaluated if SNPs causally linked to BP exhibited connections to renin-angiotensin-aldosterone system (RAAS)-associated proteins, utilizing data from the Fenland study, encompassing approximately 10 000 participants, and assessing associations among 10.2 million SNPs and 3892 proteins [6].
Subsequently, we investigated the relationship between the identified SNPs and the extended RAAS.This system is pivotal in BP regulation and encompasses a range of plasma protein targets [7].Using a proteogenomic map derived from 10 708 individuals of European descent, encompassing 4775 measured plasma proteins, and identified protein quantitative trait loci (pQTL), we specifically investigated associations with RAAS component proteins [6].Our findings revealed that rs17677724 exhibited a negative association with renin (b ¼ À0.036, SE ¼ 0.018, P value ¼ 0.0435).In contrast, rs1014754 displayed a positive association with kallistatin (b ¼ 0.031, SE ¼ 0.013, P value ¼ 0.0225).These observations suggest a potential counterregulatory mechanism in response to the elevated BP associated with these SNPs.
This study identified novel loci associated with hypertension, including FBN2 and JPH2.Previous studies supporting the correlation of FBN2 with BP traits, particularly in heart tissue, underscore its role in shaping the passive mechanical properties of major arteries, contributing to early-onset hypertension [8].FBN2 demonstrates predominant expression during embryonic and postnatal life.This early-life significance elucidates its association with the early onset of hypertension in our study, explaining its ) noted as an intergenic SNP and the index, intergenic, SNP rs73350117 (P ¼ 2.82 Â 10 À9 ).On the right, the plot focuses on the JPH2 region, demonstrating rs1014754 (P ¼ 1.26 Â 10 À9 ), identified as an intronic SNP in the time-to-event GWAS analysis (N ¼ 144 286).Linkage disequilibrium (LD) information is depicted as a colour overlay, representing the level of linkage disequilibrium measured by r 2 , with red indicating high LD (0.8-1.0) and purple indicating low LD (0.0-0.2).The secondary y axis on the right of each plot illustrates recombination rates retrieved from the UCSC (University of California, Santa Cruz) Genome Browser.These plots were generated using the 'locuszoomr' R script available at https://github.com/Geeketics/LocusZooms. GWAS, genome-wide association study; LD, linkage disequilibrium.identification by SPACOX [8].Additionally, our findings highlight the link between increased JPH2 expression in the adrenal gland and hypertension, a connection that aligns with the pivotal role of tissue-specific voltage-gated L-type calcium channel (LTCC) isoforms in aldosterone biosynthesis [9].JPH2 facilitates the recruitment of functional LTCCs to the membrane, plausibly via direct interaction with the channel [9].Furthermore, it is a prominent blood pressure regulatory locus identified in previous GWAS studies (https://www.ebi.ac.uk/gwas/genes/JPH2), that serves as a crucial structural bridge between the plasma membrane and the sarcoplasmic reticulum, essential for proper excitation-contraction coupling in cardiac muscle cells.Mutations in JPH2 have been associated with various cardiac conditions, including hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmias, and sudden cardiac death (SCD) [10].ClinGen presently designates JPH2 with moderate evidence supporting its role in HCM under an autosomal dominant inheritance pattern, and likewise for its involvement in other cardiac conditions.Furthermore, moderate evidential backing is observed for its connection with DCM with a semidominant inheritance pattern (https://search.clinicalgenome.org/kb/genes/HGNC:14202).This study's strength lies in employing a whole genomewide event time data analysis, using the SPACOX software, to identify significant loci associated with hypertension.In the study conducted by Bi et al. [4], imputed data was utilized, whereas we employed WGS data, thereby uncovering novel loci.Variants exclusive to individuals typed solely on SNP arrays could potentially be missed through imputation, leading to unreliable outcomes [11].Our study presents a significant advancement, yielding more robust results.Additionally, our method's superior ability to detect missed loci in binary phenotyping enhances our genetic insights into hypertension.Validation of variants identified through Mendelian randomization, PheWAS, and examination of their impact on BP-related proteins in the Fenland cohort strengthens our findings.Nevertheless, limitations include the study's focus on middle-aged white participants in the UK Biobank, cautioning against broad generalizations to diverse populations, necessitating validation through diverse study designs [12].Although we recognize limitations in the SPACOX methodology, such as the inability to account for relatedness through random effects and the determination of effect sizes for SNPs, we note that the methodology is highly scalable and well calibrated for variants across various allele frequencies [4].Further analyses utilizing alternative approaches such as frailty models incorporating random effects and age-dependent liability threshold (ADuLT) models [13,14] could facilitate comparisons with the SPACOX methodology, validate findings, and offer valuable additional insights.Further investigations are crucial to corroborate our results and determine underlying mechanisms.

FIGURE 2
FIGURE 2 SPACOX whole-genome time-to-event genome-wide association study analysis.The first circos plot at the centre displays the Àlog 10 P values representing the effect of SNP alleles on hypertension.The second circos plot depicts chromosome density.Red dots indicate P values less than 5 Â 10 À8 .These plots were generated using the 'CMplot' R script available at https://github.com/YinLiLin/R-CMplot.GWAS, genome-wide association study; SPACOX, a saddle point approximation implementation based on the Cox PH regression model.

TABLE 1 .
Significant single nucleotide polymorphisms identified in the time-to-event analysis at a genome-wide association study significance level of 5 Â 10 À8 It includes the rsIDs, the chromosome on which they are located, their position (in base pairs), the gene to which they map, and the corresponding P values for both SPACOX (a Saddle Point Approximation Implementation based on the Cox PH regression model) and Cox analyses.P-(SPACOX), P value for Cox Proportional Hazards (Saddlepoint Approximation).P-(COX), P-value for Cox Proportional Hazards (Normal Approximation).CDO1, cysteine dioxygenase type 1; DIRC3, disrupted in renal carcinoma 3; FBN2, fibrillin-2; JPH2, junctophilin-2; ncRNA-intronic, noncoding RNA intronic; P-(COX), P value for Cox Proportional Hazards (normal approximation); POS, position; P-(SPACOX), P-value for Saddle Point Approximation; SNP, single nucleotide polymorphism; SLC27A6, solute carrier family 27 member 6; TRIM36, tripartite motif-containing protein 36.